Simulation Examples Research Articles

Design of Adaptive Finite-Time Backstepping Control for Shield Tunneling Systems with Constraints

This paper focuses on the finite-time tracking control problem of shield tunneling systems in the presence of constraints on the states and control input. By modeling the system based on the LuGre friction model, an effective method of tracking control in finite time is designed to overcome these actual constraints at the same time. First, the constraint on the system state is transformed into a symmetric constraint on the tracking error, and the constraint on control input is handled by designing an auxiliary differential equation. Then, radial basis function (RBF) neural networks are introduced to approximate the uncertainties. Next, using an adaptive finite-time backstepping method and choosing a logarithmic barrier Lyapunov function (BLF), a finite-time controller is designed to realize the finite-time stability of the closed-loop system. Finally, a simulation example is given to verify the correctness and validity of the theoretical results.

Using the evolution of knowledge to explain changes in farm size distribution and specialization

AbstractThis study developed a theoretical model to explain the consolidation of farmland and specialization in agriculture. In the model, farmers are assumed to be endowed with knowledge regarding the production of each crop, which evolves through learning. Knowledge across crops is substitutable to various degrees. The farm size distribution and specialization are shown to be equilibrium outcomes determined by the distribution of knowledge. A simulation example based on farm‐level acreage data from California is presented to show the relevance of learning mechanisms and test model implications.

Hidden corrosion monitoring via phase shift evaluation of Lamb wave modes

Lamb wave inspection is a promising candidate for real-time monitoring of hidden corrosion in thin-walled metallic structures. The time-of-flight (ToF) which represents the variation of group velocity due to thickness reduction is commonly used, but its sensitivity is limited at the early stage of corrosion. To address this issue, the phase shift as a function of the phase velocity is defined and evaluated for corrosion detection in this paper. Specifically, the analytic cross-correlation method is applied, which estimates the group delay and the phase shift between two Lamb wave responses before and after the existence of corrosion simultaneously. The simulated examples show that the phase shift performs an obvious improvement on corrosion sensitivity compared to the ToF, and its value increases nearly linearly as the corrosion gets deeper. Ultimately, an experimental example is also introduced, where an active sensor network is deployed and a probabilistic imaging algorithm is introduced. The imaging results demonstrate that the proposed method could successfully identify and accurately localize the hidden corrosion.

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness.

Observer-based adaptive memory event-triggered consensus tracking control for high-speed train under DoS attacks

This paper focuses on the event-triggered consensus tracking of high-speed trains under denial-of-service (DoS) attacks. A novel anti-disturbance control strategy is proposed, based on an adaptive memory state observer and a disturbance observer. A memory is introduced to buffer system output information which enhancing the precision of the state observer, and the gust disturbance during the high-speed train operation is estimated by the disturbance observer. The Linear Matrix inequality technique is used to obtain the observer feedback coefficient and the controller gain, and the Lyapunov theory is employed to demonstrate the controller’s consistency tracking performance. Applying the memory to the event-triggered mechanism, a new adaptive memory event-triggered scheme is designed to better ensure system performance and effectively prevent the occurrence of Zeno behavior. To achieve precise identification of DoS attacks in the event-triggered environment, a new detection algorithm is proposed. Finally, simulation examples are used to validate the effectiveness and practicality of the proposed scheme.

Analysis and solution of unstable heat equation based on output feedback

In this paper, the output feedback-based bipartite consensus problem for MASs of unstable heat equations is investigated. The interactions of participating individuals are cooperative and competitive on an undirected graph. In order to achieve accurate estimation of system state, a distributed state observer is proposed. By introducing a reversible transformation, the observer system is transformed into a new system, and the controller based on the new system states is designed, which guarantees the well-posedness and stability of the considered control system and the observer system due to the invertibility of the presented transformation. Similarly, the bipartite consensus is analysed and proved. Finally, the validity of the proposed method is verified by simulation examples.

Model following adaptive control for nodes in complex dynamical network via the state observer of links

Geometrically, a complex dynamical network (CDN) can be regarded as the interconnected system composed of the node subsystem (NS) and the link subsystem (LS) coupled with each other. Guided by this idea, in order to achieve the goal of each node following asymptotically its own reference target in a CDN, this paper investigates the model following adaptive control (MFAC) problem of NS via the dynamics of links, which implies that the LS plays the important dynamic auxiliary role in the MFAC realization of nodes. Meanwhile, we focus on the condition that the links state information is unavailable, due to sensor practical application and measurement cost constraints. To obviate this restriction, we construct the asymptotical state observer for the LS. Next, to achieve the control goal of this paper, an appropriate Lyapunov candidate function is selected, by which the MFAC scheme for NS is synthesized based on the state observer of LS. Finally, the simulation example is performed to demonstrate the theoretical results in this paper.

Composite‐observer‐based asynchronous control for hidden Markov nonlinear systems with disturbances

SummaryIn this article, an asynchronous adaptive tracking control approach is presented for a type of hidden Markov jump nonlinear systems with external disturbances. In this joint jump process model, hidden Markov model signifies the dynamics of the actual system, whereas the signal emits from the detector symbolizes the transmitted information. This leads to the phenomenon of asynchronization between the modes of the system and that of the controller. Accordingly, an asynchronous observer is developed by using the mode information from the detector to develop an asynchronous control approach. The observer contains a disturbance estimation part, to compensate the unknown external inputs. Utilizing the backstepping scheme, a strict‐feedback asynchronous tracking controller is formulated, guaranteeing that all signals within the closed‐loop system are semi‐globally uniformly ultimately bounded in probability. Finally, the validity of the presented methodology is illustrated by means of a simulation example.

Quantum‐Noise‐Driven Generative Diffusion Models

AbstractGenerative models realized with Machine Learning (ML) techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion Models (DMs) are an emerging framework that have recently overcome Generative Adversarial Networks (GANs) in creating high‐quality images. Here, is proposed and discussed the quantum generalization of DMs, i.e., three Quantum‐Noise‐Driven Generative Diffusion Models (QNDGDMs) that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non‐trivial interplay among coherence, entanglement, and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, the suggestion is to exploit quantum noise not as an issue to be detected and solved but instead as a beneficial key ingredient to generate complex probability distributions from which a quantum processor might sample more efficiently than a classical one. Three examples of the numerical simulations are also included for the proposed approaches. The results are expected to pave the way for new quantum‐inspired or quantum‐based generative diffusion algorithms addressing tasks as data generation with widespread real‐world applications.

Quantized control for interconnected PDE systems via mobile measurement and control strategies

This paper deals with the Rϵ-stabilization for interconnected partial differential equation systems (IPDESs) by mobile measurement and control strategies. First, compared to the static measurement and control strategies, the mobile ones are employed to improve the dynamic response of the closed-loop system, while reducing the number of sensors and actuators effectively. Moreover, considering the constrained transmission channel bandwidth, the hysteresis quantizer with an adjustable parameter is introduced to save the communication resources in the network, while can balance the quantization effect and the control performance by adjusting the parameter. Further, the output quantization controller is designed to ensure that the closed-loop system is Rϵ-stable, which converges to a given interval in finite time. Finally, a simulation example of chemical non-isothermal tubular reactors is provided to illustrate the validity of the presented design method.

Edge-based event-triggered consensus control of multi-agent systems against DoS attacks

This article focuses on the security consensus control for general linear multi-agent systems against denial of service (DoS) attacks. In particular, an edge-based event-triggered protocol is proposed to reduce unnecessary information transmission through energy-limited and fragile networks, where the dynamic event-triggered condition is designed by introducing internal dynamic variables. The state of agents is dynamically estimated, and the control inputs based on estimated value can offer better control performance. It is demonstrated that under DoS attacks, the security consensus of multi-agent systems can be realised without Zeno behaviour by adopting the dynamic edge-based event-triggered control strategy. Finally, a numerical simulation example is included to prove the effectiveness of the proposed method.

Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models

This article proposes an adaptively bounded gradient descent (ABGD) algorithm for group elastic net penalized regression. Unlike previously proposed algorithms, the proposed algorithm adaptively bounds the Fisher information matrix, which results in a flexible and stable computational framework. In particular, the proposed algorithm (i) does not require orthogonalization of the predictors, and (ii) can be easily applied to any combination of exponential family response distribution and link function. The proposed algorithm is implemented in the grpnet R package (available from CRAN), which implements the approach for common response distributions (Gaussian, binomial, and Poisson), as well as several response distributions not previously considered in the group penalization literature (i.e., multinomial, negative binomial, gamma, and inverse Gaussian). Simulated and real data examples demonstrate that the proposed algorithm is as or more efficient than existing methods for Gaussian, binomial, and Poisson distributions. Furthermore, using two genomic examples, I demonstrate how the proposed algorithm can be applied to high-dimensional multinomial regression problems with grouped predictors. R code to reproduce the results is included as supplementary materials.

Relay feedback identification and PI-PD control of asymmetrical heating and cooling processes

A common type of nonlinear system in the field of process industry is asymmetrical heating and cooling processes. These systems have two operating modes, heating and cooling, each with a particular dynamic feature. This study deals with identifying and controlling such processes. The relay feedback identification method based on the state-space approach, which results in exact estimates, is used in the identification procedure to achieve two first-order plus dead time (FOPDT) process models. Following process identification, two sets of PI-PD controller parameters are acquired in line with the user-defined gain and phase margin specifications, and the system is controlled using a gain-scheduled PI-PD control method. Some simulation examples are provided to demonstrate the use and profitability of the suggested identification and control approaches. Comparisons with a similar study are provided for both identification and control approaches to clearly demonstrate the benefits of the suggested procedures.

Event-triggered secure consensus for second-order nonlinear multiagent systems against asynchronous DoS attacks

This paper investigates secure consensus of the second-order nonlinear multiagent systems under malicious asynchronous DoS attacks by using event-triggered control. Due to the presence of diverse attackers, network protocols, and different defense strategies, asynchronous launched DoS attacks are always considered in communication and control channels. Moreover, to alleviate the pressure on communication and computing resources, a distributed event-triggered control strategy is presented, where the control strategy relies on the agent’s own and its neighbors’ information. Then, several secure consensus criteria for multiagent systems with directed and undirected topologies are established based on the designed distributed controller and different Lyapunov functions. Furthermore, it is demonstrated that the trigger sequences eliminate Zeno behavior by utilizing a hybrid event-triggered strategy. Finally, the usefulness of the achieved outcomes is substantiated via two simulation examples.

Set-membership state estimation for time-varying complex networks: two zonotopic design methods

This article studies the zonotopic set-membership state estimation problem for linear time-varying complex networks with unknown-but-bounded (UBB) noises, where the UBB noises are contained by a set of zonotopes. The objective of the addressed problem is to give two design methods, namely the correction matrix method and the state observer method, where a time-varying zonotopic sequence containing all possible states of the system is obtained. The expressions of the correction matrix and the observer gain are given under the F-radius criterion, and the desired minimum zonotopes are obtained. In addition, a state observer based on the measured output at the current moment is designed to analyze the equivalence between the above two methods. Finally, in order to demonstrate the effectiveness of the proposed state estimation algorithms, two simulation examples are presented.

Improved switching condition for reachable set estimation of discrete-time switched delayed neural networks

This research delves into the reachable set estimation (RSE) problem for general switched delayed neural networks (SDNNs) in the discrete-time context. Note that existing relevant research on SDNNs predominantly relies on either time-dependent or state-dependent switching approaches. The time-dependent versions necessitate the stability of each subnetwork beforehand, whereas the state-dependent switching strategies solely depend on the current state, thus disregarding the historical information of the neuron states. For fully harnessing the historical information pertaining to neuron states, a delicate combined switching strategy (CSS) is formulated with the explicit goal of furnishing a relaxed and less conservative design framework tailored for discrete-time SDNNs, where all subnetworks can also be unstable. By resorting to the established time-dependent multiple Lyapunov–Krasovskii functional (TDMLF) technique, the improved criteria are subsequently presented, ensuring that the reachable set encompassing all potential states of SDNNs is confined to an anticipated bounded set. Ultimately, the practicality and superiority of the presented RSE approach are thoroughly validated by two illustrative simulation examples.

Adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems with unmeasurable states

This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Fixed‐time adaptive event‐triggered fault‐tolerant control of nonlinear systems with actuator failures

AbstractThe problem of fixed‐time adaptive event‐triggered fault‐tolerant control (FTC) is investigated for a class of nonlinear systems with actuator failures in this paper. Firstly, by utilizing back‐stepping algorithm and the fixed‐time stable criterion, a novel fixed‐time adaptive fault‐tolerant controller and parameter updated laws in forms of nonlinear differential equations are developed to counteract actuator faults and parameter uncertainties in fixed time. Then, the event‐triggered mechanism based on the relative threshold method is applied to the proposed control scheme to save network resources. Furthermore, by the Lyapunov stability theory, it can be proven that all the closed‐loop signals of system remain bounded, and the tracking error converges into a small neighborhood of the origin within fixed time interval. Finally, the simulation examples are shown to verify the feasibility and efficiency of the presented strategy.

A novel modeling approach for the magnetorheological brake system based on improved LSTM

In order to model the magnetorheological brake system under long-term operation and different working conditions, a novel performance prediction approach based on an improved long short term memory (LSTM) model is proposed to solve this problem. The framework of the proposed approach is presented, and an improved sparrow search algorithm is designed to optimize the hyperparameters of LSTM. Moreover, the proposed prediction approach based on improved LSTM is designed and the flowchart of this approach is shown. In addition, the first simulation example was carried out to demonstrate the effectiveness of the proposed model compared with the artificial neural network model and the conventional geometric model. Finally, the other simulation example was designed to exhibit the superior performance of the proposed algorithm compared with other algorithms.

Data-driven-based sliding-mode dynamic event-triggered control of unknown nonlinear systems via reinforcement learning

This paper investigates a data-driven-based dynamic event-triggered control problem for continuous-time unknown nonlinear systems using the reinforcement learning method and the sliding-mode surface technique. Initially, by constructing a cost function associated with sliding-mode surface variables for the nominal system, the original control problem is equivalently transformed into a problem of designing a dynamic event-triggered optimal control policy. To handle the unknown issue of system dynamics, a data-driven model is established to reconstruct the system dynamics. Then, under the framework of reinforcement learning, a critic network is employed to solve the event-triggered Hamilton–Jacobi–Bellman equation. The weight vector in the critic network is updated through the current data and historical data, such that the persistence of excitation condition is no longer needed. After that, it is strictly proven via Lyapunov stability theory that all the signals of the considered system are bounded in the sense of uniformly ultimately boundedness. Finally, the effectiveness of the developed control method is demonstrated by two simulation examples.